
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "69"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 124 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 119 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.370785	-0.224164	-0.740542	-0.634638	1.274910	5.335619	-1.395387	-0.759651	0.7704	0.7658	0.2347	17.732788	15.184590
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	2.15%	17.20%	0.045796	-1.275587	0.160463	-0.150080	0.027249	3.614308	-1.259052	-0.839332	0.6953	0.6844	0.2237	3.315736	2.865161
2459592	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	0.644929	-0.273753	0.279894	-0.326065	0.681544	4.841025	-1.892800	-0.086688	0.0473	0.0466	0.0098	nan	nan
2459591	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	2.70%	-0.432640	-1.411720	0.041813	0.701922	0.304583	2.044897	-1.154403	-0.706728	0.6027	0.6081	0.3250	1.799373	1.758361
2459590	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	2.16%	-0.248189	-1.205459	0.094918	0.490075	2.035573	1.681143	-0.757216	-0.286451	0.6045	0.6077	0.3184	2.049389	1.956643
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	20.97%	0.156810	-0.861185	-0.908325	-0.044918	-0.569417	1.209142	-1.370434	-1.131524	0.6745	0.6601	0.2435	2.445712	2.300589
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	6.45%	0.084425	-1.467653	0.451224	0.044005	-1.083653	3.591432	-1.326678	-1.198549	0.7497	0.7303	0.2050	5.779524	4.154450
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	-0.514005	-0.909575	8.792045	4.990304	-23.333991	-23.858603	-4.675000	-4.393628	nan	nan	nan	12.408781	5.135309
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	3.24%	15.14%	-0.278103	-1.479890	1.110977	-0.535144	1.284828	-0.092217	-1.351918	-1.094890	0.6021	0.6138	0.3158	1.796961	1.681756
2459585	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.521069	-1.056567	1.988573	-1.208800	0.357866	-0.720872	-1.796292	-0.157952	0.0425	0.0385	0.0036	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.698596	3.815206	10.603442	0.095876	21.529626	3.212610	35.490366	37.801071	0.5685	0.5745	0.3186	0.000000	0.000000
2459583	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.340251	-1.410281	4.523803	-0.716732	4.211560	1.812592	-1.050752	0.734865	0.6081	0.6133	0.3370	0.000000	0.000000
2459582	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.292472	-0.982716	4.224075	-0.642410	4.762710	1.960341	-1.181719	-0.536936	0.5986	0.6009	0.3385	3.210261	2.947711
2459581	digital_ok	0.00%	0.00%	0.00%	0.00%	3.24%	16.22%	-0.575368	-1.582671	3.749999	-0.516019	3.472529	2.152114	-1.158809	-0.302482	0.6175	0.6203	0.3326	2.047440	1.909156
2459580	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.187259	-1.237512	4.745053	-0.398813	4.660493	1.855133	-1.254213	0.075996	0.6080	0.6186	0.3333	0.000000	0.000000
2459579	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.242561	-0.894729	5.569810	-0.625030	4.765541	2.135964	-1.199705	0.088171	0.6024	0.6128	0.3367	6.019696	4.936514
2459578	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	1.370257	0.690937	1.403010	-0.077049	2.532993	9.230187	0.253173	-0.140123	0.0439	0.0401	0.0039	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	15.14%	-0.284291	-1.199865	3.635127	-0.629425	3.425462	3.569467	-0.831886	0.070907	0.6131	0.6202	0.3308	2.056367	1.929485
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	10.75%	-0.318136	-1.044424	2.530570	-0.749735	3.428367	2.000935	-1.245240	-0.630523	0.6272	0.6341	0.3341	1.760108	1.722728
2459575	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	7.57%	0.018676	-1.012588	2.568743	-0.947029	1.618337	2.712017	-0.914066	-0.292970	0.6281	0.6343	0.3331	1.796278	1.649044
2459573	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.463506	-0.826369	6.160185	-0.549015	5.644496	2.670117	-0.978696	0.722455	0.6239	0.6283	0.3376	3.736765	3.154954
2459572	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	32.43%	0.016576	-1.180658	2.953538	-0.757642	3.261146	1.187867	-1.501297	-0.031473	0.6265	0.6313	0.3349	2.097890	1.885383
2459571	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	1.412651	0.652625	1.489217	-0.156380	1.285969	6.525844	0.239827	0.249783	0.0412	0.0459	0.0077	nan	nan
2459570	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.486765	-0.266106	0.386857	-0.142646	0.026965	-0.378669	-0.743844	-0.883892	0.6123	0.6314	0.3178	0.649085	0.648642
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	33.51%	-0.100542	-1.214542	3.207601	-0.744161	2.507784	0.196368	2.149020	-0.910862	0.7326	0.7368	0.2034	3.299577	2.900485
2459566	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.150808	-0.982901	4.216956	-0.608052	3.099106	2.303219	-1.015819	1.240418	0.6103	0.6198	0.3513	3.293716	2.993077
2459565	digital_ok	100.00%	-	-	-	100.00%	0.00%	98361.619992	127659.769146	5154704.473226	4577120.545713	nan	nan	-5.052525	-5.052525	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	4.62%	2.724876	1.466561	3.318467	1.707234	0.516585	-0.835818	-1.800211	-1.660049	0.6141	0.6228	0.3313	1.973450	1.656600
2459563	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.731077	5.136102	25.048953	21.386172	14.405388	12.013245	-1.691865	-1.617705	0.6059	0.6180	0.3367	5.371410	4.948552
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	1.689087	0.656437	1.234625	0.390197	0.212179	-1.111261	-1.428880	-1.353837	0.6112	0.6265	0.3338	1.914499	1.733759
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	74.05%	2.283727	1.064919	1.846849	0.891527	0.713899	-0.835590	-2.068413	-2.085841	0.6176	0.6379	0.3387	5.433756	4.557953
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	65.95%	1.740077	0.838858	1.416039	0.701139	0.689923	-0.353324	-1.754199	-1.607105	0.5981	0.6118	0.3468	3.346371	2.898066
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	82.26%	2.357337	1.470357	1.949764	1.204672	1.126812	-0.040360	-1.619659	-1.490437	0.6125	0.6303	0.3315	2.875507	2.600332
2459558	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0471	0.0457	0.0062	nan	nan
2459556	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.326629	5.710861	14.207874	11.497723	14.291644	10.834790	-2.372223	-2.905716	0.6172	0.6267	0.3292	4.615652	3.908826
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6001	0.6126	0.3410	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6097	0.6220	0.3386	nan	nan
2459552	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.648777	7.919271	18.279675	16.031685	20.886523	21.435982	0.390978	0.049175	0.6218	0.6263	0.3348	0.000000	0.000000
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	72.04%	1.967702	1.133363	1.872017	0.968510	0.850767	-0.506090	-2.106775	-1.986346	0.6154	0.6253	0.3290	3.085317	2.755394
2459550	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0436	0.0388	0.0034	nan	nan
2459549	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.590908	5.169186	13.692813	11.725595	15.009718	12.052480	-2.174047	-2.258183	0.6143	0.6269	0.3403	2.623737	2.511840
2459542	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.207894	5.229526	26.867399	23.608969	0.517792	1.055920	-1.856344	-1.503908	0.6777	0.6876	0.3138	2.369110	2.179313
2459541	digital_ok	100.00%	1.34%	1.79%	0.00%	100.00%	0.00%	6.333938	4.318534	16.522470	13.905116	10.261658	10.530035	4.190134	3.882222	0.6783	0.6782	0.2929	2.754381	2.552989
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	52.23%	3.546094	1.741288	3.240740	2.071367	1.515777	1.029240	-0.921392	-0.719909	0.6838	0.6881	0.3086	2.433616	2.348350
2459536	digital_ok	100.00%	37.06%	37.06%	0.00%	100.00%	0.00%	9.320708	8.142928	21.746768	19.670149	21.129881	18.448172	-3.383021	-2.691866	0.4173	0.4199	0.2196	1.565421	1.373354
2459535	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.408160	1.213283	2.545777	1.794929	3.869031	1.045120	-0.083472	-1.256575	0.0787	0.0669	0.0124	1.161454	1.171409
2459534	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.984558	0.677086	1.733164	0.872232	0.324251	-0.125275	-1.436213	-1.214002	0.0994	0.0856	0.0162	1.162689	1.169818
2459533	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.393136	5.351504	13.975449	11.789574	12.862588	10.855870	-2.715528	-1.710417	0.0913	0.0842	0.0181	1.146765	1.157458
2459532	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.557911	6.430512	18.167957	15.194084	15.511028	13.885444	-2.270313	-1.824679	0.0840	0.0814	0.0170	0.838407	0.842466
2459530	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.780694	0.901776	1.495652	0.761312	0.689381	-0.273966	-1.222160	-1.085454	0.0926	0.0929	0.0218	1.141178	1.150893
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.046200	6.593380	14.862992	12.457381	28.257197	23.487585	-1.815243	-0.663062	0.9021	0.9361	0.2900	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	7.608818	6.252843	19.905854	17.919063	10.516227	19.824828	-5.771699	-4.845295	nan	nan	nan	5.917586	4.644174
2459522	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.594168	5.271356	18.160626	15.128349	6.311112	9.484856	-3.004363	-2.511574	0.6841	0.6928	0.3097	2.641810	2.298021
2459513	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	5.488712	4.628715	10.464395	7.747898	2.666413	29.021878	0.088565	3.563985	0.0517	0.0483	0.0094	nan	nan
2459508	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.734268	6.988406	12.124447	9.329077	12.330991	8.053331	10.739748	10.330016	0.0929	0.0743	0.0067	0.000000	0.000000
2459505	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	93.443106	120.786008	117.533561	104.455340	130.367470	89.011086	715.623125	484.272958	0.0086	0.0076	0.0041	1.099566	1.083502

Antenna Season Report Notebook¶

Antenna 69 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 69, Node 4:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 69: 2459650

Antenna 69: 2459649

Antenna 69: 2459648

Antenna 69: 2459647

Antenna 69: 2459646

Antenna 69: 2459645

Antenna 69: 2459642

Antenna 69: 2459640

Antenna 69: 2459639

Antenna 69: 2459638

Antenna 69: 2459637

Antenna 69: 2459636

Antenna 69: 2459635

Antenna 69: 2459634

Antenna 69: 2459633

Antenna 69: 2459632

Antenna 69: 2459631

Antenna 69: 2459630

Antenna 69: 2459629

Antenna 69: 2459628

Antenna 69: 2459627

Antenna 69: 2459626

Antenna 69: 2459625

Antenna 69: 2459624

Antenna 69: 2459623

Antenna 69: 2459622

Antenna 69: 2459621

Antenna 69: 2459620

Antenna 69: 2459619

Antenna 69: 2459618

Antenna 69: 2459617

Antenna 69: 2459616

Antenna 69: 2459615

Antenna 69: 2459614

Antenna 69: 2459613

Antenna 69: 2459612

Antenna 69: 2459611

Antenna 69: 2459610

Antenna 69: 2459609

Antenna 69: 2459608

Antenna 69: 2459607

Antenna 69: 2459605

Antenna 69: 2459604

Antenna 69: 2459603

Antenna 69: 2459602

Antenna 69: 2459598

Antenna 69: 2459597

Antenna 69: 2459596

Antenna 69: 2459595

Antenna 69: 2459594

Antenna 69: 2459593

Antenna 69: 2459592

Antenna 69: 2459591

Antenna 69: 2459590

Antenna 69: 2459589

Antenna 69: 2459588

Antenna 69: 2459587

Antenna 69: 2459586

Antenna 69: 2459585

Antenna 69: 2459584

Antenna 69: 2459583

Antenna 69: 2459582

Antenna 69: 2459581

Antenna 69: 2459580

Antenna 69: 2459579

Antenna 69: 2459578

Antenna 69: 2459577

Antenna 69: 2459576

Antenna 69: 2459575

Antenna 69: 2459574

Antenna 69: 2459573

Antenna 69: 2459572

Antenna 69: 2459571

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶